Internet of Things (IoT) can be defined as the system of interconnected devices and network components that are connected through the internet, without the requirement of user interference for the transferring of data, tasks, or implementation of such tasks. Things in IoT stand for all of the devices and components that are interconnected with each other and cause transfer of data and efficiency in implementation of tasks. It is defined as a web or series of interconnected devices, sensors, components whether through wires or wirelessly.
IoT term was first coined by “Kevin Ashton”, for the implementation of RFID in his employees’ business operational cycle. IoT in simple terms is a way of reducing the cost of operations and increasing the efficiency of the manufacturing and business process through transferring of data over the internet without the requirement of constant supervision from a human being.
Data management in IoT is broad term referring to the implementation and maintenance of structure of devices and users of the data generated. It contains a number of concepts and applications which can be defined as the solutions in data management such as
- Data Integration – Data integration is a process of combining technical and business processes and knowledge so that it makes some sense to the user. It helps in combining raw data with the reference information so that it can be used for informative inputs and applications in the business cycles.
- Data Analytics & Visualization – With the accumulation of data in the form of data integration in a business cycle, there is a need for visualizing and analyzing this integrated and combined data. Data visualization is nothing but visualizing of data in a graphic form so that the human eyes and brain can easily make sense of it. Data analytics is the process of finding links between the data that is generated from the devices that are connected through Internet of Things.
- Metadata Management – It is the implementation of a set of rules and regulations defined beforehand. It helps in keeping information of a particular file, along with the summarized analysis for the same.
- Data Security – Data security can be defined as the process of protecting the generated data, prohibiting the access to the personnel that do not have permission to access that specific data. It also helps keep a safe copy to prevent from any corruption of the particular file and data. It involves encryption, implementation of pre-specified rules if any by the consumer and keeping a protective copy of the data over the cloud.
- Data Migration – It is the process of transferring entirety of the generated data from one storage system to another. The complete validation of the transferred data, and the removal of that particular data from the previous storage system.
- Data Governance – Data governance can be defined as the overall umbrella process of making the data available to the users, integration of that particular data and security of that data in the data management operational cycle.
- Data Quality – Data quality refers to the implementation of conditions and variables pre-defined, if the data passes or fits all of the conditions defined the data can be deemed of high quality or passed the data quality conditional phase.
- Data Orchestration – Data orchestration is the process of accumulating real-time updates and the data generated by the brands or the consumers using the particular process. It is not classified on which device or technology the user may be using or engaging the data with. It consists of six major components – collection, transformation, enrichment & stitching, audience association, action and ownership.
These solutions available to the consumers of data management in Internet of Things are what is effectively causing such a life-changing transformation in all of our lives. Implementation of it in every aspect of our lives is not far, and with that one can hope to achieve ease of use in every aspect of their lives.
